A Learning Algorithm with Boosting for Fuzzy Reasoning Model

There have been proposed many learning algorithms for fuzzy reasoning models based on the steepest descend method. However, any learning algorithm known as a superior one does not always work well. This paper proposes a new learning algorithm with boosting. Boosting is a general method which attempts to boost the accuracy of any given learning algorithm. The proposed method consists of three sub-learners. The first sub-learner is constructed by performing the conventional learning algorithm with randomly selected data from given data space. The second sub-learner is constructed by performing the conventional learning algorithm with the data selected with equal probability from correctly and incorrectly learned data in the first learning. The third sub-learner is constructed with the data for which either the first or the second sub-learner is incorrectly learned. The output for any input data is given as decision by majority among the outputs of three sub-learners. That is, the method attempts to boost correctly learned data by learning incorrectly learned data repeatedly. In order to show the effectiveness of the proposed algorithm, numerical simulations are performed.